Data Applications
Data ApplicationsCodes
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Simple CRUD Application
import streamlit as st import pandas as pd import base64 from PIL import Image import json import urllib.request # DB Management # DB Functions # Database Creation & Connection import sqlite3 from sqlite3 import Connection import streamlit as st # Security #passlib,hashlib,bcrypt,scrypt import hashlib URI_SQLITE_DB = "Users.db" html_temp = """ <body style="background-color:red;"> <div style="background-color:tomato;padding:10px"> <h2 style="color:white;text-align:center;">Simple Login Application Code</h2> </div> </body> """ st.set_page_config( page_title = 'Apps', page_icon = "✌", layout = "centered", initial_sidebar_state = "expanded" ) def make_hashes(password): return hashlib.sha256(str.encode(password)).hexdigest() def check_hashes(password,hashed_text): if make_hashes(password) == hashed_text: return hashed_text return False # """ # check_same_thread = False is added to avoid same thread issue # """ conn = sqlite3.connect(URI_SQLITE_DB, check_same_thread=False) c = conn.cursor() @st.cache(hash_funcs={Connection: id}) def get_connection(path: str): """Put the connection in cache to reuse if path does not change between Streamlit reruns. NB : https://stackoverflow.com/questions/48218065/programmingerror-sqlite-objects-created-in-a-thread-can-only-be-used-in-that-sa """ return sqlite3.connect(path, check_same_thread=False) def create_usertable(conn: Connection): c.execute('CREATE TABLE IF NOT EXISTS userstable(username TEXT PRIMARY KEY,password TEXT)') def add_userdata(conn: Connection,username,password): c.execute('INSERT INTO userstable(username,password) VALUES (?,?)',(username,password)) conn.commit() def login_user(conn: Connection,username,password): c.execute('SELECT * FROM userstable WHERE username =? AND password = ?',(username,password)) data = c.fetchall() return data def main(): """Simple Login Application""" st.markdown(html_temp, unsafe_allow_html=True) menu = ["Home","Login","SignUp","Others","About"] st.markdown( """ <style> .sidebar .sidebar-content { # background-image: linear-gradient(#264D59, #43978D, #F9E07F, #F9AD6A, #D46C4E); background-image: linear-gradient(to right top, #ebe9f3, #b7bdcc, #8094a5, #496e7c, #0c4950); color: blue; } </style> """, unsafe_allow_html=True, ) choice = st.sidebar.selectbox("Menu", menu) if choice == "Home": st.subheader("Home Page:") elif choice == "Login": st.subheader("**Please Login to the page !**") username = st.sidebar.text_input("Username","") password = st.sidebar.text_input("Password",type='password') if st.sidebar.checkbox("Login"): hashed_pswd = make_hashes(password) result = login_user(conn,username,check_hashes(password,hashed_pswd)) if result: st.success("Logged In as {0!s}".format(username)) else: st.warning("Incorrect Adhar/Passport/VoterID/Password") elif choice == "SignUp": st.subheader("Create New Account") new_user = st.text_input("Username","") new_password = st.text_input("Password",type='password') if st.button("Signup"): create_usertable(conn) add_userdata(conn,new_user,make_hashes(new_password)) st.success("Account created successfully. Please Login to application.") elif choice == "Others": st.info("Please visit to nearest adhar or passport or voterid location to get any of the ID.") elif choice == "About": st.sidebar.title("App Version details") st.sidebar.info("**App version 1.0**") if __name__ == "__main__": main() -
PencilSketcher App
import streamlit as st import numpy as np from PIL import Image import cv2 st.set_option('deprecation.showfileUploaderEncoding', False) def dodgeV2(x, y): return cv2.divide(x, 255 - y, scale=256) def pencilsketch(inp_img): img_gray = cv2.cvtColor(inp_img, cv2.COLOR_BGR2GRAY) img_invert = cv2.bitwise_not(img_gray) img_smoothing = cv2.GaussianBlur(img_invert, (21, 21),sigmaX=0, sigmaY=0) final_img = dodgeV2(img_gray, img_smoothing) return(final_img) st.title("PencilSketcher App") st.write("This Web App is to help convert your photos to realistic Pencil Sketches") file_image = st.sidebar.file_uploader("Upload your Photos", type=['jpeg','jpg','png']) if file_image is None: st.write("You haven't uploaded any image file") else: input_img = Image.open(file_image) final_sketch = pencilsketch(np.array(input_img)) st.write("**Input Photo**") st.image(input_img, use_column_width=True) st.write("**Output Pencil Sketch**") st.image(final_sketch, use_column_width=True) -
IrisPrediction App
import streamlit as st import pandas as pd from sklearn import datasets from sklearn.ensemble import RandomForestClassifier import urllib.request from PIL import Image html_temp = """ <body style="background-color:red;"> <div style="background-color:tomato;padding:10px"> <h2 style="color:white;text-align:center;"> Classifier(SL) - Iris Flower Prediction App</h2> </div> </body> """ st.beta_set_page_config( page_title = 'Iris', page_icon = "🌺", layout = "centered", initial_sidebar_state = "expanded" ) def inputFeatures(): sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4) sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4) petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3) petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.2) data = {'sepal_length': sepal_length, 'sepal_width': sepal_width, 'petal_length': petal_length, 'petal_width': petal_width} features = pd.DataFrame(data, index=[0]) return features def main(): st.markdown(html_temp, unsafe_allow_html=True) st.sidebar.info(""" ## Packages: - Streamlit, pandas & sklearn - How to install? > 1. **pip install streamlit** > 2. **pip install pandas** > 3. **pip install scikit-learn** """) st.sidebar.header('Input Features') df = inputFeatures() st.sidebar.info("#### Iris Flower species : setosa, versicolor, virginica") img = Image.open(urllib.request.urlopen("https://raw.githubusercontent.com/SurendraRedd/MachineLearningCode/master/Iris_Species.png")) # Opens the image from the url st.sidebar.image(img, width=300, caption="") st.sidebar.info("#### Features : setosa, versicolor, virginica") img = Image.open(urllib.request.urlopen("https://raw.githubusercontent.com/SurendraRedd/MachineLearningCode/master/features.png")) # Opens the image from the url st.sidebar.image(img, width=400, caption="") st.subheader('Input Features') st.write(df) iris = datasets.load_iris() X = iris.data Y = iris.target clf = RandomForestClassifier() clf.fit(X, Y) prediction = clf.predict(df) prediction_proba = clf.predict_proba(df) st.subheader('labels & index number') st.write(iris.target_names) st.subheader('Prediction') st.write(iris.target_names[prediction]) #st.write(prediction) st.subheader('Prediction Probability') st.write(prediction_proba) if __name__ == '__main__': main()